Graph Databases (Graph DB) are widely applied in various fields, including
finance, social networks, and medicine. However, translating Natural Language
(NL) into the Graph Query Language (GQL), commonly known as NL2GQL, proves to
be challenging due to its inherent complexity and specialized nature. Some
approaches have sought to utilize Large Language Models (LLMs) to address
analogous tasks like text2SQL. Nevertheless, when it comes to NL2GQL taskson a
particular domain, the absence of domain-specific NL-GQL data pairs makes it
difficult to establish alignment between LLMs and the graph DB. To address this
challenge, we propose a well-defined pipeline. Specifically, we utilize ChatGPT
to create NL-GQL data pairs based on the given graph DB with self-instruct.
Then, we use the created data to fine-tune LLMs, thereby achieving alignment
between LLMs and the graph DB. Additionally, during inference, we propose a
method that extracts relevant schema to the queried NL as the input context to
guide LLMs for generating accurate GQLs.We evaluate our method on two
constructed datasets deriving from graph DBs in finance domain and medicine
domain, namely FinGQL and MediGQL. Experimental results demonstrate that our
method significantly outperforms a set of baseline methods, with improvements
of 5.90 and 6.36 absolute points on EM, and 6.00 and 7.09 absolute points on
EX, respectively.

使用 ChatGPT 创建自我指导的领域特定自然语言 - 图查询数据对以及使用这些数据对大型语言模型进行微调，并提出了一种提取相关模式的方法来引导模型生成准确的图查询语言（GQL）。实验证明，该方法在金融领域和医学领域的图数据库上取得了显著的性能改进。

将大型语言模型对齐到领域特定的图数据库

Aligning Large Language Models to a Domain-specific Graph Database

Graph data management and querying has many practical applications. When
graphs are very heterogeneous and/or users are unfamiliar with their structure,
they may need to find how two or more groups of nodes are connected in a graph,
even when users are not able to describe the connections. This is only
partially supported by existing query languages, which allow searching for
paths, but not for trees connecting three or more node groups. The latter is
related to the NP-hard Group Steiner Tree problem, and has been previously
considered for keyword search in databases. In this work, we formally show how
to integrate connecting tree patterns (CTPs, in short) within a graph query
language such as SPARQL or Cypher, leading to an Extended Query Language (or
EQL, in short). We then study a set of algorithms for evaluating CTPs; we
generalize prior keyword search work, most importantly by (i) considering
bidirectional edge traversal and (ii) allowing users to select any score
function for ranking CTP results. To cope with very large search spaces, we
propose an efficient pruning technique and formally establish a large set of
cases where our algorithm, MOLESP, is complete even with pruning. Our
experiments validate the performance of our CTP and EQL evaluation algorithms
on a large set of synthetic and real-world workloads.

本文提出了连接树模式 (CTPs) 在图形查询语言中的整合及其算法的研究；研究广义关键字搜索，提出一种有效的修剪技术 MOLESP，并在大量实验中验证了 CTP 和 EQL 评估算法的性能。